CHAPTER 4: Trends of Online Marketing As Adopted By Indian Railways

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1 CHAPTER 4: Trends of Online Marketing As Adopted By Indian 4.1 Introduction In today s competitive environment of 21 st century, it has become necessity for Indian to bring changes in offering different tourism services with the use of advanced technologies. No doubt Indian is becoming technologically advanced and the fact that people can book their tickets online. The people who know how to access internet on computers, can easily get reservation done on the internet itself. Indian Railway Online Booking has certainly changed the system of reservation in India. The best thing about this service is that one can get reservation done, sitting at home before the computer. This chapter provides a holistic view of the major initiatives undertaken by the Indian to promote the adoption of online marketing of Indian. It also studies the pace and pattern of technological developments undertaken by Indian. To put the growth in perspective, in the seven years since its inception, the railways technological advancement initiative has witnessed spectacular growth. This chapter examines the trends of online marketing of Indian which is also the first objective of the present study. Further, this chapter is divided into four sections. The first section outlines the context of trends of online ticket reservation. The second section gives a brief idea about the different earnings through online marketing of Indian in order to provide an understanding the full potential of the internet. The evolution and trends of different online tourism services is the subject matter of third section. The last section provides the web server analysis for online marketing purposes. 4.2 Online Ticket Reservation Trends analysis In this section growth trends of online tickets booked, passengers traveled through online tickets, service charge for online ticket reservation and fare value of online tickets has been analysed. The data has been provided by Online Ticketing reservation centre of IRCTC New Delhi. It is to be noted here that data has been analysed on monthly basis since its inception to March

2 Indian provides two types of online tickets - I-tickets and E-tickets. I-ticket was introduced in August 2002, allows the traveler to place an order for a ticket, and get a physical ticket couriered to him. E-tickets (introduced in August 2005) on the other hand, allow the traveler to take a ticket print-out at their own end after booking the ticket. For heading towards journey, one is required to take the e-ticket along with an appropriate identity card I-ticket I-Ticket Booked I-ticket reservation is showing a polynomial trend. As it is clear from the figure 4.1 it is showing an upward trend up to 2008 but after that it started declining continuously. The value of R 2 is which is quite good; depicting 71.7% variation in ticket reservation is due to time. The equation of trend line is: I-ticket reservation. Y = X X to estimate the future Figure 4.1: I Ticket Booked 93

3 Passengers Travelled with I-Ticket Figure 4.2 is also showing an upward trend. The value of R 2 is 0.659; depicting 65.9% variation in passengers travelled with I-Ticket is due to time. The equation of trend line is: I-ticket passengers travelled. Y = X X to estimate the future Figure 4.2: Passengers Travelled with I-Ticket Ticket Fare The value of R 2 is 0.651; depicting 65.1% variation in ticket fare of I-Ticket is due to time. The equation of trend line is: Y = X 2 + 1E+07X 8E+07 to estimate the future I-ticket fare. Figure 4.3: Ticket Fare 94

4 Service Charge The value of R 2 is 0.727; depicting 72.7% variation in service charge of I-Ticket is due to time. The equation of trend line is: Y = -5038X X 4E+06 to estimate the future I-ticket service charge. Figure 4.4: Service Charge I-Ticket Cancelled The value of R 2 is 0.607; depicting 60.7% variation in I-tickets cancellation is due to time. The equation of trend line is: Y = X X to estimate the future I-ticket cancellation. Figure 4.5: I-Ticket Cancelled 95

5 Relationship Between I-tickets Booked and Cancelled In this section relationship between I-Tickets booked and cancelled have been analysed. As it is clear from the figure 4.6 that both the variables are moving in the same direction. Figure 4.6: Relationship Between I- Tickets Booked and Cancelled Regression analysis is performed to estimate the future i-ticket cancellation. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), I-Tickets Booked ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 5.481E E a Residual 1.934E E8 Total 7.415E10 91 a. Predictors: (Constant), I-Tickets Booked b. Dependent Variable: I-Tickets cancelled 96

6 Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) I-Tickets Booked a. Dependent Variable: I-Tickets cancelled The value of R=.860 depicts high correlation between I-tickets booked and cancelled. The value of R square is.739 explains 73.9% of variance in i-tickets cancelled due to i-tickets booked. It could be inferred from ANOVA table that overall model is fit as the p-value is significant. To estimate the regression look at the coefficient table the value of constant is but p-value is not significant so constant is not significant. The value of beta coefficient is.265 and its p-value is significant. So the regression equation is Y =.265 * X implies I-Ticket Cancelled =.265 * I-Tickets Booked E-ticket E-tickets (introduced in August 2005) on the other hand, allow the traveler to take a ticket print-out at their own end after booking the ticket. For heading towards journey, one is required to take the e-ticket along with an appropriate identity card. The which launched e-tickets in 2005 after about a year of delay since the proposal was envisaged feared that the e-tickets may be misused. E- tickets have proven to be a hit since their launch. Apart from the sheer ease of taking the print-out at the user's end without having to wait for a courier delivery, the fact that the service charge for booking e-tickets are cheaper than i-tickets also helped E-Ticket Booked E-ticket reservation is showing a power trend. Figure 4.7 is showing an upward trend continuously for e-ticket reservation. The value of R 2 is which is very good; depicting 98.7% variation in ticket reservation is due to time. 97

7 The equation of power trend line is: reservation. Y = 223.8x to estimate the future E-ticket Figure 4.7: E Tickets Booked Passengers Travelled with E-Ticket Figure 4.8 is also showing an upward trend. The value of R 2 is 0.987; depicting 98.7% variation in passengers travelled with E-Ticket is due to time. The equation of trend line is: Y = 362.3x to estimate the future E-ticket passengers travelled. Figure 4.8: Passengers Travelled with E-Ticket 98

8 E-Ticket Fare The value of R 2 is 0.988; depicting 98.8% variation in ticket fare of E-Ticket is due to time. The equation of trend line is: Y = 22366x to estimate the future E -ticket fare. Figure 4.9: E-Ticket Fare Service Charge through E-Ticket Booking The value of R 2 is 0.98; depicting 98% variation in service charge of E-Ticket is due to time. The equation of trend line is: Y = 20195x to estimate the future E-ticket service charge. Figure 4.10: Service Charge through E-Ticket Booking 99

9 E-Ticket Cancelled The value of R 2 is 0.987; depicting 98.7% variation in E-tickets cancellation is due to time. The equation of trend line is: Y = 20.93x to estimate the future E-ticket cancellation. Figure 4.11: E-Ticket Cancelled Relationship Between E-tickets Booked and Cancelled In this section relationship between E-Tickets booked and cancelled have been analysed. As it is clear from the figure 4.12 that both the variables are moving in the same direction. Figure 4.12: Relationship Between E-tickets Booked and Cancelled Regression analysis is performed to estimate the future e-ticket cancellation. 100

10 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate a E5 a. Predictors: (Constant), E-Tickets Booked ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 5.481E E a Residual 1.934E E8 Total 7.415E10 91 a. Predictors: (Constant), I-Tickets Booked b. Dependent Variable: I-Tickets cancelled Coefficients a Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) E-Tickets Booked a. Dependent Variable: E-Tickets Cancelled The value of R=.984 depicts high correlation between E-tickets booked and cancelled. The value of R square is.968 explains 96.8% of variance in e-tickets cancelled due to e-tickets booked. ANOVA table shows that overall model is fit as the p-value is significant. To estimate the regression look at the coefficient table the value of constant is and p- value is also significant so constant is significant. The value of beta coefficient is.266 and its p-value is significant. So the regression equation is Y = * X implies E-Ticket Cancelled = * E-Tickets Booked 101

11 4.2.3 Comparison between E-Ticket and I-Ticket In order to study the growth rate trends of online ticket booking yearly data have been analyzed. According to statistics available, only tickets were sold across the country when the Indian started selling tickets through a dedicated website in It was a negligible percentage of yearly sales. Table 4.1: Year Wise Online Ticket Booking and Growth Month I-Ticket Growth % E-Ticket Growth % * * Note:- * Figures are from August Figure 4.13: Comparison between E-Ticket and I-Ticket 102

12 In first four years I-Ticket has registered an upward growth trend. But surprisingly in the growth rate has decreased to 18%. In there was a decline of 26.3% instead of increase, due to the inclination of customers towards e-tickets more as compare to I-tickets. There is further decline of 51.32% in E-tickets were introduced in 2005 has shown more growth as compare to I-tickets. In a span of four years, the number of e-tickets booked had increased to from In the first year the number of e-tickets booked was very less. But, during the second year e-ticket has shown a dramatic increase of %. The growth rate in was %. But in the growth rate was only 70.23% Share of E-Tickets and I-Tickets E-tickets were introduced in 2005 has shown more growth as compare to I- tickets. In a span of five years, the number of e-tickets booked had shown a tremendous increase. In the first year the number of e-tickets booked was very less. But, after that e-ticket has shown a dramatic increase. Figure 4.14: Share of E-Tickets and I-Tickets The share of e-tickets has been increasing steadily since the service launched with e-tickets accounting for 98% per cent share in of total online train tickets, up from about 94 percent ( ), 82 per cent ( ), 58 per cent (

13 07) and 7 per cent ( ). This has further prompted the railways to further look into the possibilities of issuing more e-tickets Adoption of Online Ticket Reservation To study the adoption of online ticket reservation the data of passengers travelled with counter tickets is being compared with online tickets (E-Ticket and I-Ticket). As it is clear from the figure 4.15 that the percentage of passengers travelled with online Tickets is being increasing continuously. During the period April 07 August 07 Only 8% online passengers travelled but in Feb 09 this figure increased upto 32%. The share of e-tickets has been increasing steadily since the service launched with e-tickets accounting for 31.4% per cent share in 2010 of total train tickets, up from about 27.2 percent (2009), 24 per cent (2008), 20 per cent (2007) and 13 per cent (2006). This has further prompted the railways to further look into the possibilities of issuing more e-tickets. On the Other hand the share of I-tickets booked is reducing every year. It was only 2% in year 2002 and in 2010 it was 0.6% of total tickets booked. The inclination towards e-ticket of railways is rising spectacularly as compare to I-tickets. Figure 4.15: Adoption of Online Ticket Reservation 104

14 The number of passengers carrying e-ticket is still less than 30% of the total number of travelers. A journey in an Indian train would reveal that most passengers still carry the traditional railway counter book tickets.you are also likely to encounter the inexplicable sight of people standing in long queues in front of booking counters just to ascertain the PNR status or enquire accommodation availability in trains when all they could do was to use the internet to get the enquired result. Poor literacy levels combined with equally poor internet awareness has hindered the spread of internet bookings. There are travellers who prefer to make their reservations from counters then from the internet. Reason - they feel more secure interacting with people than with a faceless web interface. The point being made is counter bookings are often backed with support services due to which the traveller can always bank on the counter people and the relevant authorities. Online bookings however continue to grow Agents Online Ticket Booking Agents online booking (introduced in May 2006) on the other hand, allow the agents to take a ticket print-out at their own end after online booking of the ticket Agents Registered During the Month The number of agents registered during the month for online booking is showing a polynomial trend. Overall it is showing an upward trend. The value of R 2 is which is quite good; depicting 61.6% variation in agent s registration due to time. The equation of trend line is: Y = X X to estimate the future agent s registration. Figure 4.16: Agents Registered During the Month 105

15 Agents Online-Ticket Booking Agents online ticket booking is showing a polynomial trend. As it is clear from the figure 4.17 it is showing an upward trend continuously. The value of R 2 is which is very high; depicting 99% variation in ticket reservation is due to time.the equation of trend line is: Y = 1483 X X to estimate the future online booking. Figure 4.17: Agents Online-Ticket Booking Passengers Travelled through Agents Online Booking It is also showing an upward trend. The value of R 2 is 0.987; depicting 98.7% variation in passengers travelled with I-Ticket is due to time. The equation of trend line is: Y = 2794X X to estimate the future passengers travelled through agents online booking. Figure 4.18: Passengers Travelled through Agents Online Booking 106

16 Ticket Fare The value of R 2 is 0.988; depicting 98.8% variation in ticket fare is due to time. The equation of trend line is: Y = 1E+06X 2-1E+07X + 3E+07 to estimate the future ticket fare. Figure 4.19: Ticket Fare Collected by Travel agents Service Charge Of IRCTC Through Agents Online Reservation The value of R 2 is 0.99; depicting 99% variation in service charge of IRCTC through agents online booking is due to time. The equation of trend line is: Y = 25172X X to estimate the future IRCTC service charge. Figure 4.20: Service Charge Of IRCTC Through Agents Online Reservation Service charges collected by agents 107

17 The value of R 2 is 0.991; depicting 99.1% variation in service charge collected by agents through online booking is due to time. The equation of trend line is: Y = 20344X X to estimate the future service charge collected by agents through online booking. Figure 4.21: Service Charges Collected by Agents Payment Gateway Analysis Users can make payment for reservation through various modes like debit card, credit card, net banking etc. To study the success rate of the transactions performed through various options statistics for the month of March 2010 has been analyzed. Figure 4.22: Modes of Payment Most preffered mode of payment by users is Net banking/debit cards (38%) followed by credit card option (27%). Only 19% users are using other options of making the 108

18 payments. The reason being most of the users are using net banking facility, credit cards and debit cards. The other option is specifically used to make payment for railway ticket reservation only. Figure 4.23: Success of Different Modes of Payment After analyzing the above figure 4.23 it may be concluded that most successful mode is other option which includes Suvidha, Sify, Flight raja, OSSRDS, RDS and Redeem PG. Inspite of the highest success rate very less number of people are using other mode of payment. 4.3 Earnings through Online Marketing Indian is also generating earnings through various online marketing options like promotional mails, banners on site, confirmation mail, text link, PNR alert, etc. To study the trends of these earnings data from April 2007 to March 2010 has been analyzed. The value of R 2 is 0.528; depicting 52.8% variation in earnings through online marketing with the time. The equation of trend line is: Y = X X 6E + 09 to estimate the future earnings through online marketing. 109

19 Figure 4.24: Earnings through Online Marketing As we have discussed Indian have earnings through various sources but it is to be noted it has no earnings from confirmation mail and PNR alert. The earnings from other three different sources are given below Figure 4.25: Earnings through Different Sources of Online Marketing As it is clear from the above figure 4.25 that earning from text lin k is almost negligible and earnings from promotional mails is reducing. Indian railways is not having very high earnings from online marketing. 110

20 4.4 Trends of Online Travel services of Indian Indian is also providng travel services like tour packages, holiday packages, hotel facility, cab facility as well as air tickets. In this section trends of various travel services will be studied Users Registered For Online Tourism Services Indian introduced online tourism services in January The trends of users registered for online tourism services is as follows: Figure 4.26 is showing a polynomial trend for users registered for online tourism services. The value of R 2 is which is quite good; depicting 61.5% variation in users registration is due to time. The equation of trend line is: users registration. Y = X X to estimate the future Figure 4.26: Users Registered For Online Tourism Services Rail Tour Packages Monthly trend of rail tour packages is being analyzed since its introduction from January 2008 to March Users have given as option to book rail tour packages through internet as well through counter. 111

21 Figure 4.27: Monthly Trend of Rail Tour Package online and Rail Tour Package Counter Indian railway is offering rail tour packages which can be booked through counter as well as online. As it is clear from the figure 4.27 both are showing an upward polynomial trend. The numbers of online bookings are greater than counter bookings in every year. Trend Analysis of Online Rail tour Package: Y= x x ; R 2 = Trend Analysis of Counter Rail tour Package: Y= x x ; R 2 = Date Table 4.2: Growth Rate of Rail Tour Packages Rail Tour Package Online Growth % 112 Rail Tour Package Counter Jan'08-Mar' Growth It could be easily inferred from the table 4.2 that growth rate of online rail tour package is much higher as compare to counter booking of rail tour packages. In the growth rate of online booking was 66.18% as compared to only 23.06% of %

22 counter. It shows more inclination of consumers towards online services as compare to counter bookings. Figure 4.28: Comparison of Rail tour Packages The booking of online rail tour packages has been increasing since day one, as more and more people are turning to book through internet. The Indian railways have booked 305 packages online only in three months as compared to 64 through counter mere in three months in Continuing with its growth trend, the online tickets booked has gone up to 3269 during the year as against 2034 through counter. The Indian Railway had registered more than double online bookings as of counter booking in Figure 4.29: Share of Rail Tour Package online and Rail Tour Package Counter 113

23 The share of online tour packages is consistently high since the service launched. Online booking of rail tour package is accounting for 83% per cent share in 2008 of total rail tour packages, about 62 percent ( ), 68 per cent ( ), and 70 per cent ( ). This has further prompted the railways to further look into the possibilities of booking more online rail tour packages Holiday Packages Monthly trend of holiday packages is being analyzed since its introduction from January 2008 to March Users have given as option to holiday packages through internet as well through counter. Figure 4.30: Monthly Trend of Online Holiday Package and Counter Holiday Package The above figure 4.30 shows that counter booking of holiday packages are showing an upward polynomial trend on the other hand online holiday packages are not showing any kind of growth. The numbers of counter bookings are much greater than online bookings in every year. Trend Analysis of Online Holiday Package: Y= x x ; R 2 = Trend Analysis of Counter Holiday Package: Y= 0.041x x ; R 2 =

24 Date Table 4.3: Growth Rate of Holiday Packages Holiday Package Online Growth % Holiday Package Counter Jan'08-Mar' Growth % It could be easily inferred from the table 4.3 that growth rate of online holiday package is much lower as compare to counter booking of holiday packages. During the first year the growth rate of online bookings was 700% as of % of counter. But in the growth rate of online booking was only 132.5% as compared to % of counter. Surprisingly consumers are showing more inclination towards counter bookings as compare to online bookings of holiday packages. Figure 4.31: Comparison of Holiday Packages The number of holiday packages booking was very less during the first two years. In it start risingand reached to 119 bookings from only 1150 bookings in But in counter booking has shown a spectecular grwoth and registered 115

25 2901 bookings in contrast of only 261 bookings through internet. It has registered more than ten times counter bookings as of online bookings in Figure 4.32: Share of Online Holiday Package and Counter Holiday Package During the first two years the share of online holiday package booking was high. But in a drastic change took place; online booking of holiday package is accounting for only 16% per cent share and reduced to 8% in Tourist Trains Indian Railway is also offering different luxurious tourist trains to the travelers like Bharat Darshan, Buddhist and Fairy queen. Only Bharat Darshan is performing very well showing approximately 88% growths in the year On the other hand remaining two trains contribution is very less; in fact of fairy queen in 2010 was almost negligible. Figure 4.33: Tourist Trains 116

26 4.4.5 Trend of Online Cab Booking To study the trend of online cab booking monthly data for the period of January March 2011 has been analyzed. Indian railway is providing cab facility through various travel agencies. It charges certain amount of commission from them on the basis of booking of cabs. Figure 4.34: Online Cab Booking Online booking of cab is consistently showing a decreasing trend. In January 2008 there were 57 online cab bookings and this figure reduced to 18 in March So as it is very much clear that it is not performing well as compare to other online travel services. It does not seem to be very much profitable. The monthly average for online booking of cab is only 32. Trend Line of Online Cab Booking: Y= 0.023x x ; R 2 = Online Hotel Booking Indian railways introduced online hotel booking in January Indian railways is providing hotel booking through two service providers ginger hotel and clear trip hotel. But it has not shown a very good growth over a period of time. In 2009 it has registered 8534 hotel bookings and this figure reduced to 7787 in On an average it has accounted for only 697 hotel bookings in a month. It has registered a highest booking of 1049 during the month of October Trend Line of Online Hotel Booking: Y= 0.973x x ; R 2 =

27 Figure 4.35: Online Hotel Booking Online Airticket Booking through Indain Railway Website Indian railway has introduced a one more new segment online air ticket booking through its website in April It gave a very good start up with booking in first month. But it suddenly fell up to 1937 in July 2010 and it keeps on reducing up to only 573 bookings during the month March Figure 4.36: Air Segments 4.5 Web Server Analysis Average Hits per Day To study the performance of web server statistics from August 2007 to January 2010 have been analyzed. As it could be easily inferred that average hits per day is showing an increasing trend. During the month January 2010, an average hit per day has gone 118

28 up to as against during the month August The equation of trend line is: Y = 26755X 1E+10 to estimate the future average hit per day. The value of R 2 is.805 which is quite good. Figure 4.37: Average Hits Per Day Average Hits per Visitor Average hit per visitor seems to be constant. On an average every visitor perform 255 hits but during the month October 2008 this figure gone up to 1321 shows very poor performance of the website. Again there is little bit increase from July 2009 to November 2009 and this figure reached at 423 hits per visitor. Figure 4.38: Average Hits per Visitor 119

29 4.5.3 Average Visitor per Day Number of visitors is showing a polynomial increasing trend. On an average 5,74,032 persons visits the web site. During the month August 2007, 1,34,266 visitors visited the website and this has gone up to 14,36,598 in January On average every visitor views 3 pages in a day. The equation of trend line is: Y = X X + 2E+09 to estimate the future average hit per day. The value of R 2 is.884 which is quite good. Figure 4.39: Average Visitor per Day 4.6 Conclusion The Indian has made a quantum leap forward in terms of switching over from paper-based ticketing systems to online ticketing. I-ticket reservation is showing a polynomial trend. It is showing an upward trend up to 2008 but after that it started declining continuously. On the other hand E-ticket reservation is showing an upward power trend. The percentage of passengers travelled with online Tickets is being increasing continuously. Most preffered mode of payment by users is Net banking/debit cards followed by credit card option. Indian railways is not having very high earnings from various online marketing options like promotional mails, banners on site, confirmation mail, text link, PNR alert, etc. The numbers of online tour package bookings are greater than counter bookings. But in case of holiday packages the numbers of counter bookings are much greater than online bookings. Online cab booking and hotel booking are not performing well as compare to other online travel services. It does not seem to be very much profitable. On the other hand 120

30 air segments gave a very good start up but subsequently shown a very steep fall. The trend analysis revealed that though a majority of the users are using only online ticket reservation and only few of them are using other services. In nutshell, it can be concluded that Indian online marketing has come a long way just in the last 5-6 years, but still it s in early stages. It might still take some time to reach the maturity level of more developed markets. It looks more like the prebubble period. A lot of investment is happening, user base is increasing, new opportunities are coming up - things seem to be moving in the right direction. Like western world, Indian will soon face competition from other means of travel, particularly Airlines and Roadways. Competition is always good news for both players and consumers and surely must be gearing up to embrace that. 121